Application of physiologically based pharmacokinetic modeling to understand real‐world outcomes in patients receiving imatinib for chronic myeloid leukemia

Abstract We aimed to use physiologically based pharmacokinetic (PBPK) modeling and simulation to predict imatinib steady‐state plasma exposure in patients with chronic myeloid leukemia (CML) to investigate variability in outcomes. A validated imatinib PBPK model (Simcyp Simulator) was used to predict imatinib AUCss, Css,min and Css,max for patients with CML (n = 68) from a real‐world retrospective observational study. Differences in imatinib exposure were evaluated based on clinical outcomes, (a) Early Molecular Response (EMR) achievement and (b) occurrence of grade ≥3 adverse drug reactions (ADRs), using the Kruskal‐Wallis rank sum test. Sensitivity analyses explored the influence of patient characteristics and drug interactions on imatinib exposure. Simulated imatinib exposure was significantly higher in patients who achieved EMR compared to patients who did not (geometric mean AUC0‐24,ss 51.2 vs. 42.7 μg h mL−1, p < 0.05; Css,min 1.1 vs. 0.9 μg mL−1, p < 0.05; Css,max 3.4 vs. 2.8 μg mL−1, p < 0.05). Patients who experienced grade ≥3 ADRs had a significantly higher simulated imatinib exposure compared to patients who did not (AUC0‐24,ss 56.1 vs. 45.9 μg h mL−1, p < 0.05; Css,min 1.2 vs. 1.0 μg mL−1, p < 0.05; Css,max 3.7 vs. 3.0 μg mL−1, p < 0.05). Simulations identified a range of patient (sex, age, weight, abundance of hepatic CYP2C8 and CYP3A4, α1‐acid glycoprotein concentrations, liver and kidney function) and medication‐related factors (dose, concomitant CYP2C8 modulators) contributing to the inter‐individual variability in imatinib exposure. Relationships between imatinib plasma exposure, EMR achievement and ADRs support the rationale for therapeutic drug monitoring to guide imatinib dosing to achieve optimal outcomes in CML.


| INTRODUC TI ON
Imatinib, a BCR-ABL tyrosine kinase inhibitor (TKI), has significantly changed the treatment landscape of Philadelphia chromosomepositive chronic myeloid leukemia (CML). Despite showing significantly superior survival outcomes and improved tolerability compared to interferonα plus low-dose cytarabine, 1 many patients need to discontinue imatinib treatment and switch to another TKI. 2 We recently conducted a retrospective observational study of realworld patients with CML in Australia and observed that 57% (95% confidence interval [CI], 46-66%) of patients had experienced an imatinib-related adverse-drug reaction (ADR) necessitating imatinib dose changes or discontinuation, and 53% (95% CI, 42-63%) a grade ≥3 ADR, by 18 months of treatment. 2 These patients required increased monitoring and utilization of healthcare services, with many needing to commence new long-term medicines for the management of imatinib-related ADRs. 2 A large proportion of patients never achieved sustained deep molecular response (sDMR; BCR-ABL1 IS ≤0.01% maintained for at least 2 consecutive years) with imatinib treatment (5-year cumulative incidence of 41%; 95% CI, 27-55%) 2 ; a minimum requirement for attempting TKI treatment discontinuation. 3,4 Furthermore, one in four patients failed to achieve Early Molecular Response (EMR; BCR-ABL1 IS ≤ 10%) by 3 months of imatinib treatment 2 ; an endpoint predictive for major molecular response (MMR), deep molecular response (DMR) and sDMR achievement. 2,5,6 Inter-individual variability in imatinib pharmacokinetics (PK) is a possible determinant of variability in imatinib response. 7 Large inter-individual variability has been observed in imatinib trough plasma concentrations at steady-state (C ss,min ) in patients administered the same dose, with reported coefficient of variations (CV%) of 40 to 60%. [8][9][10][11][12] As a drug with a low hepatic extraction ratio, steady-state total plasma imatinib concentrations are determined by variability in intrinsic hepatic clearance (metabolism and transport) and plasma protein binding. 13 Both imatinib and its major metabolite N-desmethyl imatinib undergo mostly hepatic elimination, with biotransformation of imatinib to N-desmethyl imatinib by cytochrome P450 (CYP)3A4 and CYP2C8 a clinically important inactivating process. 7,13 Imatinib is also a substrate of P-glycoprotein (P-gp) and breast cancer resistant protein (BCRP) efflux transporters, which contribute to the net intestinal absorption of imatinib (via expression of transporters on apical/luminal membrane of enterocytes), biotransformation and excretion of imatinib (via expression of transporters on the bile caniculi of hepatocytes), 7,13 and retention of imatinib in target cancer cells. [14][15][16][17] The expression and activities of drug-metabolizing enzymes and transporters, which can be affected by genetic or environmental factors (e.g., concomitant medicine use and diet), 18 are hypothesized to influence imatinib plasma and intracellular concentrations, and thus the efficacy and tolerability of imatinib. Patients receiving imatinib treatment are likely to be receiving multiple concomitant medicines for management of their comorbidities (and treatment related ADRs), due to the older median age at CML diagnosis and expected longer survival of patients treated with TKIs. [19][20][21] As a basic compound, imatinib exhibits concentration-dependent binding with high affinity to α 1acid glycoprotein (AAG), with an unbound fraction in plasma (fu p ) of 0.05. 22 As an acute phase protein, AAG demonstrates highly variable concentrations, increasing 2 to 6-fold in response to injury, infection, inflammation and cancer. 22,23 Furthermore, there are different genetic polymorphisms of AAG, with different binding properties of variants reported. 24 Mechanistic modeling and simulation approaches can be used for predicting and understanding the PK of a drug. Physiologically based pharmacokinetic (PBPK) modeling integrates drug-specific parameters (e.g., plasma protein binding and clearance) and drug independent system-specific parameters (e.g., population variables such as organ blood flow, organ volumes and tissue composition) to simulate the time course of drug concentrations in different clinical populations. 25,26 Using a PBPK model, the drug concentration-time profile from a specific dose regimen, in a patient from a population for which clinical data was not available, can be predicted. The concentration-time profile is predicted by considering key determinants of PK in an individual patient, such as geographic ancestry, weight, age, kidney and hepatic function, and genetic polymorphisms in drug-metabolizing enzymes or transporters. PBPK modeling can also be used to understand possible drugdrug interactions, [25][26][27][28][29][30] and to predict characteristics that influence inter-individual variability in drug exposure. 31,32 Identifying factors that contribute to achievement of EMR and occurrence of imatinib-related ADRs can help inform initial imatinib dose selection, identify patients that may benefit from an alternative TKI, and inform the use of therapeutic drug monitoring (TDM) to guide dose adjustments.
The aim of this study is to investigate predictions of imatinib steady-state area under the concentration-time curve (AUC ss ), maximum concentration (C ss,max ), C ss,min , and apparent clearance (CL/F) using a PBPK model in virtual patients with the demographic and clinical characteristics identical to those patients in the real-world observational cohort study, 2 and to investigate the association between predicted plasma exposure and clinical outcomes (severe ADRs, failure to achieve EMR). The PBPK model will also be used to provide mechanistic insight into factors contributing to variability in imatinib PK, and to explore the PK implications of concomitant medicines identified in the real-world patient cohort study.

| ME THODS
A schematic representation of the study workflow is presented in

| Prediction of imatinib exposure in patients based on clinical outcomes
A previously developed and validated PBPK model of imatinib in adults 34 38 plasma AAG concentrations reported in patients with chronic phase chronic myeloid leukemia (CML; 1200 ± 120 mg L −1 at diagnosis, and 1100 ± 60 mg L −1 at follow-up) [39][40][41] are closer to those reported in healthy individuals (mean 910 ± 40 mg L −1 ), 38 corresponding to a similar unbound fraction in plasma (fu p ) of imatinib in healthy individuals and patients with chronic phase CML. 41 The corresponding dissociation constant was fixed and assumed to be independent of plasma AAG concentrations, with the variability in fu p reflecting interindividual variability in AAG concentrations. The intrinsic clearances of imatinib to N-desmethyl imatinib and other metabolites were estimated from in vitro kinetic data using recombinant CYP3A4 and human liver microsomes (HLM, in the presence of azamulin, a selective irreversible inhibitor of CYP3A4), with HLM (supplemented with azamulin) representing the CYP2C8 metabolic pathway given the assumed minor contribution (3%) of other CYP enzymes to imatinib metabolism. 42 The mechanism-based inhibition of CYP3A4 following multiple doses of imatinib was modeled by an increase in degradation of the active enzyme and thus, a decrease in enzyme activity over time, based on a turnover model detailed elsewhere. 43 P-gp and BCRP-mediated biliary clearance of imatinib were modeled using data from previous in vitro studies. 44,45 The renal clearance (CL R ) value for imatinib was taken from a study in patients with CML and Philadelphia chromosomepositive acute lymphatic leukemia. 46 Model assumptions are further detailed in Adiwidjaja et al. 34  F I G U R E 1 Schematic representation of the workflow of this study. ADR, adverse drug reaction; AUC, area under the plasma concentration-time curve; C max , maximum (peak) plasma concentration; C min , minimum (trough) plasma concentration; CYP, cytochrome P450; EMR, early molecular response; PBPK, physiologically based pharmacokinetic.
2. Occurrence of a grade ≥3 imatinib-related ADR within the followup period (defined using the Common Terminology Criteria for Adverse Events version 5 grading scale 48 ) In each scenario, individual patient parameters were added into the PBPK model for simulation of individual patient imatinib plasma concentration-time profiles and estimation of AUC ss (μg h mL −1 ), C ss,max (μg mL −1 ), C ss,min (μg mL −1 ) and CL/F (L h −1 ). Parameters used for virtual patients matched the real-world patient cohort 2 with respect to age (years), sex, actual body weight (ABW in kg; if available), A total of 10 virtual trials for each simulation were carried out.
A multiple-dosing regimen of imatinib given for 14 days was used to achieve steady-state. Potential differences in imatinib AUC ss , C ss,max, C ss,min and CL/F within different clinical scenario outcomes (i.e., between those who achieved EMR and those who did not) were evaluated by the independent two sample t-test or the Kruskal-Wallis rank sum test (if residuals were not normally distributed). A C ss,min of 1002 ng mL −1 was the exposure target for clinical response used to interpret the simulated exposures, with previous studies correlating this threshold with a significantly higher probability of MMR achievement. 9,52 All reported p values are two-sided, with a significance level of 0.05. All statistical analyses were conducted using R (version 3.3.3). 53

| Sensitivity analyses
Sensitivity analyses were conducted to evaluate the influence of variation in input parameters on the simulated imatinib plasma concentration at steady-state. 33,54,55 These analyses were conducted using either (a) the automated sensitivity analysis tool available in the Simcyp Simulator, or (b) repeated simulations using a trial design with virtual age and sex-matched subjects, whilst adjusting values of the input parameter of interest (a total of 10 virtual trials for each subgroup within the input parameter, with 10 subjects per trial).
Parameters selected for sensitivity analyses included sex, age, ABW, kidney or hepatic dysfunction (eGFR and cirrhosis, respectively), hepatic and intestinal CYP3A4 enzyme abundance, hepatic CYP2C8 enzyme abundance, hepatic P-gp transporter abundance, hepatic BCRP transporter abundance, and AAG abundance.
To investigate the influence of variation in kidney function on imatinib exposure, default "Sim-RenalGFR_less30" and "Sim-RenalGFR_30to60" populations were used. Sensitivity analyses were also conducted using the modified kidney dysfunction populations (adjusted CYP2C8 and CYP3A4 abundances) described in Supplementary Methods. To investigate the influence of variation in hepatic function on imatinib exposure, default "Sim-Cirrhosis-CP" populations were used. 56 The values for AAG abundance, drugmetabolizing enzyme abundances, and transporter abundances  59 Different types of liver damage have been shown to affect hepatocellular transport abundance to various extents. 59,60 Compared to healthy livers, hepatic P-gp abundance was reported to increase up to 350% in cholestatic liver disease, and up to 410% in autoimmune hepatitis, with no significant differences observed in fatty liver disease, alcoholic liver disease, or hepatic virus Cinduced liver damage. 59,60 Conversely, hepatic BCRP abundance appears preserved in the these types of liver damage. 59,60 If examining hepatic transporter abundance according to the functional state of the liver, a Child-Pugh class C score has been reported to result in a 260% increase in protein abundance of P-gp, with no change in BCRP abundance. 60 To determine whether model input parameters had statistically significant effects on imatinib PK parameters (AUC ss, C ss,max , C ss,min and CL/F), statistical analyses were conducted as described in the previous subsection. The parametric tests for comparing two (the independent two sample t-test) or more groups (one-way analysis of variance) were used. If the assumptions of normality and homogeneity of variances were not met, the corresponding non-parametric tests (Wilcoxon-Mann-Whitney and Kruskal-Wallis rank sum test, respectively) were used.

| Drug-drug interaction simulations
Imatinib is a substrate of CYP3A4 and CYP2C8, 13 and has also been shown to inhibit its own CYP3A-mediated metabolism after multiple-dose administration. 42 As such, PBPK simulations were performed to predict the extent of interaction between imatinib and CYP3A4/CYP2C8 modulators frequently used by patients in the real-world retrospective observational study 2 ; verapamil (a moderate CYP3A4 inhibitor), diltiazem (a moderate CYP3A4 inhibitor), trimethoprim (a weak CYP2C8 inhibitor), clopidogrel (moderate CYP2C8 inhibitor) and hyperforin (a CYP3A4 inducer in intestinal enzymes). The default PBPK model library files for verapamil and its metabolite norverapamil, diltiazem and its metabolite N-desmethyldiltiazem, and trimethoprim were used as included in the Simcyp Simulator. The previously verified PBPK models of clopidogrel and its clopidogrel glucuronide metabolite 61 were used to evaluate the potential CYP2C8 mediated drug-drug interaction with clopidogrel and imatinib. A verified PBPK model of hyperforin 62 was used to predict the potential drug-drug interaction between St John's Wort and imatinib.
The simulations were performed using the default "Sim-NEurCaucasian" population with a multiple-dosing regimen of imatinib 400 mg daily for 14 days, with the trial design representative of the demographic characteristics of patients in the real-world observational study (10 subjects, 4 females, age range 20-91 years). 2 The CYP modulators were simulated at clinically relevant dose regimens for 14 days, except for trimethoprim which was dosed for 7 days to reflect clinical practice guidelines when treating urinary tract infections. A total of 10 virtual trials for each simulation were carried out to capture population variability. The interaction between imatinib and CYP modulators at steady-state are reported as the geometric mean ratio of simulated PK parameters of imatinib (AUC ss , C ss,max , C ss,min and CL/F) with and without the perpetrator drugs (CYP modulators), and their associated 90% confidence intervals. If the predicted ratio of PK parameters fell within 1.25-fold (0.80-1.25), the interaction was not considered clinically significant. 63 Key protein targets and ligands in this article are hyperlinked to corresponding entries in http://www.guide topha rmaco logy. org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY, 64 and are permanently archived in the Concise Guide to PHARMACOLOGY 2019/20. 65

| Prediction of imatinib exposure in patients based on clinical outcomes
Individual patient characteristics, imatinib dose regimens and simulated PK parameters are presented in Supplementary Tables 3 and 4.
The simulated geometric mean plasma concentration-time profiles of imatinib at steady-state for different clinical scenarios are summarized in Table 1 and Figures 2 and 3. Notably, the simulated geometric mean AUC during 24 h after the dose (AUC 0-24,ss ), C ss,max and C ss,min of imatinib were significantly higher in patients who achieved EMR at 3 months, compared to those who did not (p < 0.05). Unlike patients who did not achieve EMR, the geometric mean C ss,min of patients who achieved EMR fell above the efficacy target of 1002 ng mL −1 . 9,52 Moreover, imatinib AUC 0-24,ss , C ss,max , and C ss,min were significantly higher in patients who experienced a grade ≥3 ADR on imatinib treatment compared to those who did not (p < 0.05).

| Sensitivity analyses
The impact of various parameters on simulated imatinib systemic exposure is shown in Supplementary Figure 1. The simulated plasma concentration-time profile of imatinib was sensitive to variation in subject age, in which a change in age from 20 to 55 years increased imatinib AUC 0-24,ss by 30% (p < 0.05) and increased C ss,min by 46% (p < 0.05). The effect was notably larger in older age, with a change in age from 55 to 90 years increasing imatinib AUC 0-24,ss and C ss,min 2-fold and 2.6-fold, respectively (p < 0.05). Increased ABW (45-125 kg) was associated with significantly decreased imatinib AUC TA B L E 1 Summary of PBPK model predicted PK parameters of imatinib following multiple-dose regimens, in patients with CML followed in the real-world retrospective observational study, 2

| Drug-drug interaction simulations
The results of the simulations to predict the interactions between selected CYP modulators and imatinib are detailed in Figure 4 and Supplementary Table 5. Notably, the AUC 0-24,ss and C ss,min of imatinib were predicted to increase by 23% and 34%, respectively, in the presence of trimethoprim. In the presence of clopidogrel, the AUC 0-24,ss of imatinib was simulated to increase by 32% and C ss,min predicted to increase by 54%. Meanwhile, the interactions between imatinib and diltiazem (AUC 0-24,ss ratio = 1.04), and between imatinib and verapamil (AUC 0-24,ss ratio = 1.06) were predicted to be non-

| DISCUSS ION
This study utilized a PBPK approach to predict the imatinib plasma concentration-time profile in patients with CML included in a realworld study to investigate whether imatinib systemic exposure differed for patients who achieved EMR or experienced severe ADRs. The PBPK model predicted significantly higher imatinib AUC ss , C ss,max and C ss,min in patients who achieved EMR at 3 months,  66 Many studies have correlated imatinib C ss,min with complete cytogenetic response (CCyR) and MMR achievement. 8,9,11,52,[67][68][69][70][71][72][73] In a European cohort study of patients treated with imatinib for at least 12 months (n = 68), mean (± standard deviation [SD]) C ss,min was significantly higher in those who had achieved CCyR than in those who had not (1123 ± 617 ng mL −1 vs. 694 ± 556 ng mL −1 , respectively; p < 0.05), and again higher in the group that had achieved MMR than in the group who had not (1452 ± 649 ng mL −1 vs. 869 ± 427 ng mL −1 , respectively; p < 0.001). 9 A pharmacokinetic analysis of the TOPS study (n = 240) showed significantly faster times to MMR, and higher rates of both MMR and CCyR at 6 and 12 months, when C ss,min ≥ 1165 ng mL −1 . 67 Imatinib C ss,min > 1000 ng mL −1 predicted improved MMR, eventfree and failure-free survival in a cohort of Chinese patients with F I G U R E 3 PBPK model simulated PK parameters of imatinib in patients treated in the real-world retrospective observational study, 2 grouped by achievement of EMR or occurrence of a grade ≥3 imatinib-related ADR. Simulation results are depicted as geometric mean predicted C ss,max , C ss,min , AUC 0-24,ss and CL/F of imatinib (and 95% confidence intervals) in patients who experienced the clinical outcome of interest (blue line) and in patients who did not experience the clinical outcome of interest (orange line) in the retrospective observational study. 35 patients achieved EMR, whilst 10 did not achieve EMR. 47 patients experienced a grade ≥3 ADR, whilst 21 did not. *Statistically significant difference (p < 0.05; Kruskal-Wallis rank sum test). ADR, adverse drug reaction; AUC 0-24,ss , area under the plasma concentrationtime curve from time zero to 24 h after a dose at steady-state; C ss,max , maximum (peak) plasma concentration at steady-state; C ss,min , minimum (trough) plasma concentration at steady-state; CL/F, apparent clearance; EMR, early molecular response at 3 months; PBPK, physiologically based pharmacokinetic; PK, pharmacokinetic. CML (n = 110), with those who achieved MMR showing higher mean imatinib C ss,min compared with those not achieving MMR (1474 ± SD 419 ng mL −1 vs. 986 ± 213 ng mL −1 , respectively; p < 0.001). 73 A study in Japanese patients with CML treated with imatinib (n = 254) observed a significantly lower probability of achieving MMR in patients with C ss,min < 1002 ng mL −1 . 52 Another major finding from these PBPK simulations was the significantly higher mean simulated imatinib systemic exposure in CML patients who experienced a grade ≥3 imatinib-related ADR, compared to patients who did not. Previous studies have correlated imatinib plasma concentration with toxicity; 8,66,67,74 however, a consistent threshold concentration has not been  for periorbital and limb oedema. 73 The TOPS study (n = 240) noted a significantly higher incidence of all-grade neutropenia, anemia, leukopaenia, rash, diarrhea, arthralgia/myalgia and all-cause oedema in patients with a C ss,min > 3180 ng mL −1 . 67 Further research is required to define the concentration-threshold for imatinibrelated ADRs, which may help facilitate TDM-guided dose adjustments and improve tolerability outcomes.
The predicted exposures for the various treatment outcome groups were a consequence of the differences in patient and medication-related factors, and relationships between these covariates, that could be accommodated within the PBPK modeling and simulation software. Sensitivity analyses provided mechanistic insight into the factors contributing to inter-individual variability in imatinib PK. The predicted imatinib plasma concentration-time profile was sensitive to changes in ABW, in line with previous studies where higher ABW was correlated to increased imatinib apparent volume of distribution (V/F) and CL/F, [75][76][77][78] and lower C ss,min . 8,67 Another body size metric, body surface area (BSA), has been demonstrated as an important covariate of imatinib PK variability. 8,67,79 Accordingly, in both univariable and multivariable analyses in the retrospective observational study, 2 lower ABW was associated with higher rates of occurrence of grade ≥3 ADR during imatinib treatment.
Sensitivity analysis identified age as another influential factor contributing to variability in imatinib PK. Older age was associated with reduced imatinib CL/F and increased C ss,min , C ss,max and AUC ss , confirming results from other PK studies. 8,80 The effect of age on imatinib PK supports the findings of the retrospective observational study, 2 whereby older age was associated with superior sDMR rates in univariable regression, and a higher Charlson Comorbidity Index score (of which age is included as a predictor) was associated with increased grade ≥3 ADRs in both univariable and multivariable regression. 2 Interestingly, sex was identified as an influential factor in the variability of imatinib C ss,max , with no significant influence on other PK parameters. Previous studies in patients with CML have observed significantly higher imatinib C ss,min in females compared to males, with authors highlighting differences in body weight and BSA as potential contributors. 67,81,82 Regression analysis from the observational study presented in Adattini et al 2 identified female sex as independently predictive of recurrent imatinib-related grade ≥3 ADRs, which could be attributed to lower ABW (median 64.5 vs. 82 kg in males, p < 0.001). Although not accounted for in these PBPK simulations, sex differences in expression levels of hepatic P-gp transporters may also play a role in altered imatinib PK, 59 however, the evidence is conflicting. 83 In line with previous studies, the simulated imatinib plasma concentration was sensitive to changes in AAG abundance. A reduction in AAG concentration leads to an increase in the unbound fraction of imatinib and a reduction in total plasma imatinib concentrations, due to the rapid distribution of unbound imatinib into extravascular space and increased total clearance. 77,84-90 Elevated AAG concentrations in patients have also been correlated with delayed response or resistance to imatinib treatment in CML, with studies suggesting that higher AAG concentrations are associated with reduced distribution into leukemic cells. 23,86,88 As discussed previously, AAG demonstrates highly variable concentrations, increasing 2-to 6-fold in response to injury, infection, inflammation, and cancer. 22,23 Males have significantly higher mean AAG concentrations than females. 91 There are also inter-ethnic differences in AAG concentrations, with individuals of Asian ancestry reported to have lower AAG concentrations than Europeans. [91][92][93] Additionally, drug-drug interactions can influence protein binding and subsequently the unbound concentration of a drug, as observed in interaction between clindamycin and imatinib. 85 Liver dysfunction was identified in these PBPK simulations as influential on imatinib PK, with increasing severities of liver dysfunc-  [8][9][10][11][12][13][14]. Imatinib was administered at 400 mg daily for 14 days in a North European Caucasian population, with 10 subjects (4 females) and an age range of 20-91 years. Simulation results are depicted as geometric mean predicted plasma concentrations of imatinib in the presence and absence of coadministration with the CYP modulator. The extent of interactions was evaluated based on predicted pharmacokinetic parameter ratios (ratio in the presence and absence of CYP modulators) and their 90% confidence intervals (CI). A ratio of 1 indicates absence of drug interactions with imatinib. AUC 0-24,ss , area under the plasma concentration-time curve from time zero to 24 h after a dose at steady-state; C ss,min , minimum (trough) plasma concentration at steady-state; CYP, cytochrome P450; PBPK, physiologically based pharmacokinetic; QID, four times a day; TDS, three times a day.
imatinib-related haematologic toxicities and more frequent dose reductions have also been observed in patients with pre-existing liver dysfunction. 94 The decline in activity of CYP enzymes in enterocytes and hepatocytes is more pronounced as the severity of cirrhosis increases, 95  In vitro studies have demonstrated reduced intracellular concentrations of imatinib, and reduced phosphorylation of the BCR-ABL1 downstream effector phospho-CRKL, in BCR-ABL1 positive and P-gp positive cells-a possible mechanism of imatinib resistance. [14][15][16][17] Further research is required to investigate the CML cell intracellular concentration-response relationship. Another limitation of this study is that potential non-adherence to imatinib treatment could not be accounted for. An assumption was made that patients took the prescribed dose 100% of the time leading up to the outcome of interest. Due to the prolonged (potentially life-long) course of CML treatment, adherence is critical to achieving and maintaining adequate response. There is a clear correlation between adherence to TKI treatment and achieving optimal efficacy outcomes. [109][110][111][112] Studies in patients with CML reported as few as 14% to 33% of patients were highly adherent to their TKI treatment. 109,[113][114][115] Therefore, poor adherence could be an additional factor contributing to the lack of achievement of molecular response outcomes in the retrospective observational study. 2

This work was supported by the Peter Coates Postgraduate
Scholarship in Ethnopharmacology provided by GlaxoSmithKline.

CO N FLI C T O F I NTER E S T S TATEM ENT
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

E TH I C S S TATEM ENT
The real world clinical data included in this study was collected with the approval of the Sydney Local Health District Human Research Ethics Committee (reference number: LNR/17/CRGH/248).

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly avail-